Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
According to several surveys, asset misappropriation stands out to be the most common type of occupational fraud. The purpose of this study is to develop a reliable and valid construct for risk of asset misappropriation and to assess the association between bankers’ demographic factors and their perception on the risk of asset misappropriation.
This is a cross sectional study conducted on personnel of ten financial institutions in
Malaysia in the year 2013. The fraud triangle theory underpins the development of the survey instrument. A survey questionnaire was designed and administered to 553 bankers out of which 334 were usable responses. Cochran’s method was adopted for sample size determination. Face to face data collection procedure with the help of research assistants were undertaken to improve the response rate which turned out to be sixty percent. The data collected were cleaned and issues on reliability, validity and normality of distribution were resolved. From an initial list of twenty two possible items that represent the risk of asset misappropriation, a final list of eleven items was found to be conclusive after testing for confirmatory factor analysis (chi square = 4.47; GFI =
0.93; CFI = 0.97; NFI = 0.96; TLI = 0.95; RMSEA = 0.10), normality (skewness < 1.00) and reliability (Cronbach alpha = 0.96; composite reliability = 0.96; average variance extracted = 0.70). Subsequently, a one way ANOVA was employed to test for significant associations between the bankers’ demographic characteristics and their perception on the risk of asset mis appropriation. It was found that bankers’ age (p = 0.01), working experience (p = 0.01), marital status (p = 0.01) and academic qualification (p = 0.10) had significant bearings on their perception of the risk of asset misappropriation within the financial institutions.
The Auditing Standard (ASA 240) defines asset misappropriation as a type of fraud that is often perpetrated by employees in relatively small and immaterial amounts (Coram et al., 2008). Asset misappropriation consists of fraudulent disbursement, skimming revenues and payroll fraud (Wells,
2007). Based on the PwC Global Economic Crime Survey (2009), asset misappropriation is the theft of assets, including monetary assets, cash, supplies and equipment by directors and employees for their own benefit. Fraud has been a world-wide problem for many years and it affects not only the victims, but also companies in a very broad spectrum. The Association of Certified Fraud Examiners
(ACFE) found that United States’ organisations lost 7% of their annual revenues to fraud, which indicated a stunning loss of US$994 billion (Ramamoorti and Dupree, 2010). KPMG (2009) reported an increase from 62% to 66% from year 2004 to 2008 regarding the opinion of respondents on the severity of fraud problems in Malaysia. In neighbouring countries such as India (86%), Thailand
(71%) and Australia (55%), fraud is also regarded as a major problem based on the same survey.
*Mohamad Afzhan Khan Bin Mohamad Khalil, Business School, Open University Malaysia, Email : afzhankhan@oum.edu.my
**Dr. Anuar Bin Nawawi, Faculty of Accountancy, University Teknologi MARA, Email : anuar217@salam.uitm.edu.my
***
Dr. Nurmazilah Dato’ Mahzan, Faculty of Business and Accountancy, University of Malaya, Email : nurmazilah@um.edu.my
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
The ACFE (2012) in their latest study reported that asset misappropriation is getting from bad to worse. Skimming (203 cases), cash larceny (152 cases), billing (346 cases), expense reimbursement
(201 cases), cheque tampering (165 cases), payroll (129 cases) and cash register disbursement (50 cases) were among the types of asset misappropriation reported in that global survey. Asset misappropriation has received very little attention in Malaysia except for Ahmad and Norhashim
(2008) and Liew et al. (2011). The study of Ahmad and Norhashim (2008) performed an exploratory factor analysis in validating the asset misappropriation instrument. Thereupon, at the present time, there is a need for a study to extend knowledge by performing a confirmatory factor analysis on this respective construct.
According to several surveys, asset misappropriation stands out as the most common type of occupational fraud. The purpose of this study is to develop a reliable and valid construct for risk of asset misappropriation and to assess the association between bankers’ demographic characteristics and their perception on the risk of asset misappropriation.
The measurement of asset misappropriation was first developed by Hillison et al. (1999) through a thorough literature review. Later on, many studies had used the items mentioned in Hillison et al.
(1999) to measure the risk of asset misappropriation. The study of Ahmad and Norhashim (2008) and Liew et al. (2011) in the Malaysian environment had somewhat referred to the measurement provided by Hillison et al. (1999). In a recent study by Agarwal and Medury (2014), the measurement items were again listed in their literature review. Although an exploratory factor analysis on this measurement was done by Ahmad and Hashim (2008), there is very little evidence on the utilisation of a confirmatory factor analysis to validate this construct. This study will contribute extensively to the field of fraud auditing by reconstructing and validating this research instrument. The items mentioned by previous studies (Agarwal and Medury, 2014; Hillison et al., 1999) are disclosed in Table 1.
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
Table 1: Measurement of the Risks of Asset Misappropriation
(Agarwal and Medury, 2014; Hillison et al., 1999)
A17
A18
A19
A20
A21
A22
A7
A8
A9
A10
A11
A12
A13
NO
A1
A2
A3
A4
A5
A6
A14
A15
A16
STATEMENTS
Received checks forged
Recording goods not returned and stealing cash
Cash sales shown as credit sales and cash stolen
Discount on sales not given but shown in books and money siphoned off
Credit sales collected but not recorded
Writing off receivables as bad debts and stealing the cash received
Collusion between buyer and seller to process refunds for goods not returned
Stealing assets, stores and spares, raw material, and finished goods
Sales not done but invoiced and goods stolen
Selling waste and scrap material and pocketing receipts
Including fictitious employees on pay roll and taking out their proceeds
Embezzling payroll and other tax withholdings
Encashing unused payroll checks
Encashing unused dividend pay checks
Unauthorized overtime shown and cash withdrawn
Charging personal purchase to company by misusing purchase orders or organizational credit cards
Diverting advances to personal use
Special price or privilege to customers and suppliers against kickbacks
Paying false invoices obtained through collusion with suppliers
Altering bank deposits
Stealing cash funds
Bank account manipulations to give benefit to one at the cost of the other and taking kickbacks.
The statistical analysis of Liew et al. (2011) showed some of the common causes of white-collar crime in Malaysia using the agency and self control theories. With a sample size of 300, using the convenience sampling methodology, a one way analysis of variance (ANOVA) was performed in their study. They found that certain demographic factors (age, marital status, education level and occupation) have influenced the respondents’ views on the common causes of white collar crime.
This present study will attempt to narrow the analysis gap of Liew et al. (2011) by using the fraud triangle theory (Cressey, 1953) as the underpinning theory and asset misappropriation as the dependent variable. The fraud triangle theory that underlies this paper states that asset misappropriation is committed by individuals due to three factors viz. opportunity, pressure and rationalisation. Furthermore, improper internal control system, lack of usage of technology tools and ineffective whistleblowing processes provide ample opportunity for fraud to take place. In addition, professional skepticism and a questioning mind are needed from fraud auditors and managers to assess the risks of fraud occurrence which may arise from personal financial pressure of potential fraud perpetrators. Moreover, acceptable codes of ethics are needed and they should be communicated to every person in an organisation to ensure that these people do not use rationalisation as a reason to commit asset fraud.
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
As could be seen below in Figure 1, a literature review was first done to develop the initial research questionnaire. Later, the questionnaire was pretested in a series of interviews with six experts (vice president of risk management department, government auditor, internal auditor, operational auditor, lecturer and senior lecturer in auditing). The interviews were carefully transcribed and a thematic analysis was performed to record the opinions obtained from the interviews. This led to some changes made in the research questionnaire after a focus group discussion with two academicians who were content experts. The research instrument was then pilot tested using quantitative methodology. Out of 55 questionnaires given to bankers, only 40 were usable for the pilot analysis.
Preliminary assessment was validity and reliability was performed. From the analysis, the research questionnaire was further modified. The modified research questionnaire was then subjected to another scrutiny by way of an independent external reviews by five reviewers deemed to be specialists in the field of economics, finance, statistics, fraud auditing and accounting to solicit their independent opinions. The five reviewers were a university professor, an associate professor, a certified statistician and two other senior academics with PhD qualifications. The survey questionnaire was subjected to further fine tuning. The final research questionnaire was then ready for administration. The survey questionnaires were administered to 553 bankers out of which 334 were usable responses. The computation of sample size was done according to the recommendations provided by previous studies (Bartlett et al., 2001; Krejcie and Morgan, 1970).
Face to face data collection procedure with the help of nineteen data collection assistants was undertaken to improve the response rate which turned out to be sixty percent. The data collected were cleaned and issues on reliability, validity and normality of distribution were resolved. The summary of methodology is provided below in Figure 1.
Figure 1: Development of Research Instrument
4

Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
Structural equation modelling (SEM) has been used by a number of researchers. There are many applications embedded within the model, one of which is the confirmatory factor analysis (CFA). This study has confirmed the validity and reliability of the risk of asset misappropriation construct using the
CFA mechanism. Figure 2 below describes the structural equation model developed in this study.
Based on this model, the indices for the CFA were determined. The indices are illustrated in Table 2.
Figure 2: The Structural Equation Model for the Risk of Asset Misappropriation
Construct e1
1 e2
1 e3
1 e4
1 e5
1 e6
1 e7
1 e8
1 e9
1 e10
1 e11
1
AM11 AM10 AM9
1
AM8 AM7 AM6 AM5 AM4 AM3 AM2 AM1
ASSET
MISAPPROPRIATION
Root mean square error of approximation (RMSEA) 0.10
Table 2: Confirmatory Factor Analysis Indices for the Risk of Asset
Misappropriation Construct
Index
Chi square
Observed
Indices
4.47
Recommended
Indices
Below 5.00
Goodness of fit index (GFI) 0.93
Normed fit index (NFI)
Tucker Lewis index (TLI)
0.96
0.95
Above 0.90
Above 0.90
Above 0.80
Comparative fit index (CFI) 0.97 Above 0.90
Below 0.10
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
As evident from Table 2 above, the confirmatory factor analysis ( chi square = 4.47; GFI = 0.93; CFI
= 0.97; NFI = 0.96; TLI = 0.95; RMSEA = 0.10
) fall within the suggested ranges by previous researchers (Chinda and Mohamad, 2008; Hooper et al., 2008; Singh, 2009; Wheaton et al, 1977).
The closest confirmatory factor analysis results that could be compared to the risk of asset misappropriation of this study was provided by Kung and Huang (2013) who reported their findings
(RMR = 0.046, GFI = 0.953, RMSEA = 0.067). Illegal activities and dubious behaviours were measured in their respective research.
The indices obtained in this present study are almost identical to the findings of Kung and Huang
(2013). Assessment of normality for the items representing the risk of asset misappropriation in this study indicates robust results as the figures are within the suggested ranges proposed by previous researchers (Lei and Lomax, 2005; Weinberg and Abramowitz, 2002). For a normal distribution, the
Kurtosis should be between 0 and 3 (Lei and Lomax, 2005) and skewness should be between -2 to
+2 (Weinberg and Abramowitz, 2002). Based on the explanation of Hair et al. (1998), convergent validity is measured by examining the composite reliability and the average variance extracted from the measures. The measures for reliability in this study are above 0.80 as recommended by Zikmund et al. (2010) and the average variance extracted is above 0.50 as suggested by Hair et al. (1998).
The observed results of this study (composite reliability = 0.96; average variance extracted = 0.70) prove that the convergent validity requirements of the instrument are met. Moreover, the internal consistency (Cronbach alpha = 0.96) is also assured (Zikmund et al., 2010). Collectively, the aforementioned indices suggest that the construct for the risk of asset misappropriation which was proposed by previous studies (Agarwal and Medury, 2014; Hillison et al., 1999) is normal, valid and reliable. The discussion above satisfactorily shows that the first objective of this study has been achieved.
Table 3: One Way ANOVA between Bankers’ Demographics and the Risk of Asset
Misappropriation in the Banking Sector
Demographics ANOVA Welch Brown-Forsythe
Age 0.01
*
Working experience 0.01
*
Gender
Marital status
Position
0.89
0.01
0.58
*
Academic qualification 0.10
*
Working department 0.49
0.01
0.01
0.89
0.01
0.41
0.01
0.44
*
*
*
*
0.01
0.01
0.89
0.01
0.56
0.01
0.48
*
*
*
*
* Significant p-value
This study has adopted the one way analysis of variance (ANOVA) to satisfy the second research objective, which is to assess the association between the studi ed bankers’ demographics and the risk of asset misappropriation in the Malaysian banking sector. Table 3 depicts the one way ANOVA statistical results. The significance level is set at 10% based on the recommendation by Zikmund et al. (2010). Previous researchers have adopted p < 0.01, p < 0.05 and p < 0.10 as their cutoff points in justifying significant relationships. However, this study will uphold the argument of Zikmund et al.
(2010) in picking a cutoff threshold of ten percent significance level (p < 0.10). Previous fraud studies have also adopted a ten percent significance level (Kaminski et al., 2004).
6

Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
From the one way ANOVA test results as depicted in Table 3, it is apparent that only the bankers’ age, working experience, marital status and academic qualification have influenced their perception of the risk of asset misappropriation in the Malaysian banking sector. Complementary to the one way
ANOVA results, the Welch and Brown-Forsythe test results are also significant for age, working experience, marital status and academic qualification, further strengthening the argument in favour of the associations. The other demographic characteristics tested such as gender, position and working department do not influence the bankers’ perception on the risk of asset misappropriation in their organisations. The findings of this study are very similar to that of Liew et al. (2011) except that their study had adopted a different instrument measuring white collar crimes. From an additional post hoc assessment, several conclusions can be made to further explain the results of this ANOVA test
(Table 3). People who are in their early 20s believe that the risk of asset misappropriation is slightly prevalent in their organisation whereas people in their late 30s view it the other way around.
Secondly, people with diploma and degree qualifications as compared to professional qualifications provide contrasting opinions. The findings provide evidence that bankers with diploma and degree qualifications are fairly neutral in agreeing to the presence of the risk of asset misappropriation in their organisations. In other words, they are neither in agreement nor in disagreement. Thirdly, there is a significant difference between the views of married bankers and single bankers. The seven point
Likert scale in the measurement of the items within the construct indicates that the higher the mean, the more the respondents agree that there is a perceived risk of asset misappropriation within their organisations. Married bankers’ perception (mean = 2.56) and single bankers’ perception (mean =
3.04) illustrate that married bankers tend to disagree more than their single counterparts on the presence of the risk of asset misappropriation in the organisations they are working in. It is also noted that the mean score for the perception of single bankers is closed to neutral. Inference can be made here that single bankers are more skeptical than married bankers. It is not known why marital status influences such a perception. Finally, the lesser the bankers’ working experience, the more they believe that the risk of asset misappropriation exists in their organisations. This means that young bankers see fraud as a prevalent risk as compared to bankers with more than twenty years’ of working experience. With these findings, the second research objective has been duly met.
Three possible recommendations can be derived from this study. Firstly, there is a need to conduct a qualitative study to ascertain the underlying causes of the findings in this study. This will further strengthen the knowledge in this area. Secondly, this study can be replicated in full or partially in different industry settings and environments. More studies in this area will provide useful comparisons for a more advanced assessment on the issue at stake. Thirdly, future researchers may adopt the validated instrument developed in this study to propagate further investigations. Perhaps the relationship between the risk of asset misappropriation and a host of other factors, such as whistleblowing, corporate governance, internal control system and culture could be examined using the instrument developed in this study. Table 4 below enumerates the measurement of the risk of asset misappropriation as one of the outcomes of this study. This new instrument improves the one proposed by previous researchers as shown in Table 1.
7

Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
Table 4: Measurement of the Risk of Asset Misappropriation
NO STATEMENT
AM1 Stealing cash funds processed or on hand is common in the banking sector.
AM2 Stealing the cash received without any recording is common in the banking sector.
AM3 Stealing a portion of the cash received by understating sales is common in the banking sector.
AM4 Altering bank deposits is common in the banking sector.
AM5 Writing off receivables and stealing the cash received from the written off account is common in the banking sector.
AM6 Conspiracy between buyers and sellers to process refunds for goods not returned is common in the banking sector.
AM7 Stealing merchandise, tools, supplies, and other assets are common in the banking sector.
AM8 Selling waste and scrap materials and pocketing the proceeds is common in the banking sector.
AM9 Setting up non-existing employees (phantom employees) on the payroll records and taking their pay cheques is common in the banking sector.
AM10 Manipulating payroll records to divert wages, payroll taxes, or pay cheques is common in the banking sector.
AM11 Charging personal purchases to the bank through misuse of the bank’s credit cards is common in the banking sector.
Agarwal, G.K and Medury, Y. (2014). Internal Auditor as Accounting Fraud Buster. IUP Journal of
Accounting Research and Audit Practices, Jan 01, 13(1), pp 7-29
Ahmad, Z., and Norhashim, M. (2008.) The control environment, employee fraud and counterproductive workplace behaviour: An empirical analysis. Communications of the IBIMA,
(3), pp 145-155.
Bartlett, J. E., Kotrlik, J. W., and Higgins, C. C. (2001). Organisational Research: Determining
Appropriate Sample Size in Survey Research. Spring, 19(1), Retrieved December 03, 2013,
From http://www.osra.org/itlpj/bartlettkotrlikhiggins.pdf
Chinda, T., and Mohamed, S. (2008). Structural equation model of construction safety culture.
Engineering, Construction and Architectural Management, 15(2), pp 114
–131.
Coram, P., Ferguson, C., and Moroney, R. (2008). Internal audit, alternative internal audit structures and the level of misappropriation of assets fraud. Accounting and Finance, 48(2008), pp 543-
559.
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
Cressey, D. (1953). Other people’s money; a study in the social psychology of embezzlement.
Glencoe, IL, Free Press.
Hillison, W., Pacini, C., and Sinason, D. (1999). The internal auditor as fraud-buster. Managerial
Auditing Journal, 14(7), pp 351
–363.
Hooper, D., Coughlan, J., and Mullen, M. R. (2008). Structural Equation Modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), pp 53
–60.
Kaminski, K. A., Wetzel, T. S., and Guan, L. (2004). Can financial ratios detect fraudulent financial reporting? Managerial Auditing Journal, 19(1), pp 15
–28.
KPMG (2009). KPMG Fraud Survey 2009 Report. Malaysia
Krejcie, R. and Morgan, D. (1970), “Determining sample size for research activities”, Educational and
Psychological Measurement, 30, pp 607-10
Kung, F.H., and Huang, C.L. (2013). Auditors' moral philosophies and ethical beliefs. Management
Decision, 51(3), pp 479
– 500
Lei, M., and Lomax, R.G. (2005). The effect of varying degrees of non-normality in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 12(1), pp 1-27
Liew, S. W., Puah, C., and Entebang, H. (2011). White-collar crime: a statistical study on its common causes. Social Sciences 2(4), pp 44
–49.
PwC Global Economic Crime Survey (2009)
Ramamoorti, B. S., and Dupree, J. (2010). Continuous controls monitoring can help deter and prevent fraud. Financial Executive, 26(3), pp 66-68.
Singh, R. (2009). 'Does my structural model represent the real phenomenon?: A review of the appropriate use of structural equation modelling (SEM) model fit indices'. The Marketing
Review, 9 (3), pp 199-212.
Weinberg, S.L., and Abramowitz, S.K. (2002). Data analysis for the behavioural sciences using
SPSS. Cambridge, U.K: Cambridge University Press.
Wells, J.T. (2007), Corporate fraud handbook, John Wiley and Sons, Inc., New Jersey, (2), pp 2-400.
Wheaton, B., Muthen, B., Alwin, D., F., and Summers, G. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8 (1), pp 84-136.
Zikmund, W.G., Babin, B., Carr, J., and Griffin, M. (2010), Business Research Methods. International
ISE Edition, South-Western, Cengage Learning 8 th
Edition.
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
Model Fit Summary
CMIN
Model
Default model
NPAR
30
CMIN
160.827
DF
36
P
.000
CMIN/DF
4.467
Saturated model 66 .000 0
Independence model 11 4065.495 55 .000 73.918
RMR, GFI
Model RMR GFI AGFI PGFI
Default model .084 .925 .862 .504
Saturated model .000 1.000
Independence model 1.898 .164 -.003 .137
Baseline Comparisons
Model
NFI
Delta1
RFI rho1
IFI
Delta2
TLI rho2
CFI
.960 .940 .969 .952 .969 Default model
Saturated model 1.000 1.000 1.000
Independence model .000 .000 .000 .000 .000
Parsimony-Adjusted Measures
Model
Default model
PRATIO PNFI PCFI
.655 .629 .634
Saturated model .000 .000 .000
Independence model 1.000 .000 .000
NCP
Model
Default model
Saturated model
NCP
124.827
.000
LO 90
89.221
.000
HI 90
167.981
.000
Independence model 4010.495 3804.995 4223.252
FMIN
Model
Default model
FMIN
.483
F0
.375
LO 90
.268
HI 90
.504
Saturated model .000 .000 .000 .000
Independence model 12.209 12.044 11.426 12.682
RMSEA
Model
Default model
RMSEA
.102
Independence model .468
LO 90
.086
.456
HI 90
.118
.480
PCLOSE
.000
.000
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
AIC
Model
Default model
Saturated model
AIC
220.827
132.000
BCC
223.070
136.935
BIC
335.161
383.535
CAIC
365.161
449.535
Independence model 4087.495 4088.317 4129.417 4140.417
ECVI
Model
Default model
ECVI
.663
LO 90
.556
HI 90
.793
MECVI
.670
Saturated model .396 .396 .396 .411
Independence model 12.275 11.658 12.914 12.277
HOELTER
Model
Default model
Independence model
HOELTER
.05
106
7
HOELTER
.01
122
7
Regression Weights: (Group number 1 - Default model)
Estimate S.E. C.R. P Label
AM11 <--- ASSET_MISAPPROPRIATION 1.000
AM10 <--- ASSET_MISAPPROPRIATION .944 .046 20.715 *** par_1
AM9 <--- ASSET_MISAPPROPRIATION .940 .043 21.783 *** par_2
AM8 <--- ASSET_MISAPPROPRIATION .881 .049 17.866 *** par_3
AM7 <--- ASSET_MISAPPROPRIATION .935 .047 19.858 *** par_4
AM6 <--- ASSET_MISAPPROPRIATION .961 .047 20.520 *** par_5
AM5 <--- ASSET_MISAPPROPRIATION .947 .045 21.277 *** par_6
AM4 <--- ASSET_MISAPPROPRIATION 1.016 .048 21.106 *** par_7
AM3 <--- ASSET_MISAPPROPRIATION .961 .046 20.729 *** par_8
AM2 <--- ASSET_MISAPPROPRIATION .925 .048 19.182 *** par_9
AM1 <--- ASSET_MISAPPROPRIATION .899 .053 16.939 *** par_10
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Proceedings of 10th Global Business and Social Science Research Conference
23 -24 June 2014, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-55-9
Standardized Regression Weights: (Group number 1 - Default model)
Estimate
AM11 <--- ASSET_MISAPPROPRIATION .867
AM10 <--- ASSET_MISAPPROPRIATION .850
AM9 <--- ASSET_MISAPPROPRIATION .872
AM8 <--- ASSET_MISAPPROPRIATION .780
AM7 <--- ASSET_MISAPPROPRIATION .829
AM6 <--- ASSET_MISAPPROPRIATION .845
AM5 <--- ASSET_MISAPPROPRIATION .861
AM4 <--- ASSET_MISAPPROPRIATION .857
AM3 <--- ASSET_MISAPPROPRIATION .849
AM2 <--- ASSET_MISAPPROPRIATION .813
AM1 <--- ASSET_MISAPPROPRIATION .755
Assessment of normality (Group number 1)
Variable
AM1
AM2
AM3
AM4
AM5
AM6
AM7
AM8
AM9
AM10
AM11
Multivariate min max skew c.r. kurtosis c.r.
1.000 7.000 .802 5.981 -.437 -1.630
1.000 7.000 .818 6.105 -.400 -1.492
1.000 7.000 .742 5.534 -.456 -1.702
1.000 7.000 .822 6.131 -.388 -1.448
1.000 7.000 .825 6.155 -.261 -.973
1.000 7.000 .755 5.632 -.329 -1.228
1.000 7.000 .791 5.899 -.299 -1.115
1.000 7.000 .822 6.131 -.232 -.865
1.000 7.000 .923 6.889 -.031 -.116
1.000 7.000 .901 6.722 -.086 -.321
1.000 7.000 .832 6.206 -.256 -.954
91.437 49.406
12